Transforming Catastrophe Risk Management with Cognitive AI

Table of Contents

1. Introduction
2. Minerva: A Vancouver-based company
3. Minerva's Approach to Artificial Intelligence
   • Training the Computer to Think Like Human Experts
   • Knowledge Graphs: An Alternative to Neural Networks
4. Applications of Minerva's Cognitive AI in Insurtech
   • Catastrophe Risk Modeling and Management
   • Flood Risk Assessment in Property and Casualty Insurance
   • Connecting Investors with the Right Properties
   • Wildfire Risk Analysis for Linear Infrastructure Operators
5. Benefits of Knowledge Graphs in Natural Hazard Analysis
   • Encoding Expert Knowledge in the Computer
   • Flexibility in Incorporating Quantitative and Qualitative Data
   • Rich Data Storage Format
   • Complementing Existing Cat Model Outputs
   • Transparency and Explainability of Results
6. The Five-Step Process of Minerva's Cognitive AI Workflow
   • Building the Knowledge Graph
   • Integrating Relevant Data Sets
   • Capturing Expert Knowledge
   • Applying Reasoning with the Reasoner
   • Delivering Results through Web Dashboards
7. Success Factors for Data Analysts and Scientists in the Industry
8. Conclusion

Minerva's Approach to Artificial Intelligence and Insurtech Applications

In today's rapidly evolving world, artificial intelligence (AI) has become a buzzword across various industries. Minerva, a Vancouver-based company, has developed a unique approach to AI that focuses on cognitive reasoning and leveraging knowledge graphs. In this article, we will explore Minerva's approach to AI, its specific applications in Insurtech, and the benefits of using knowledge graphs in natural hazard analysis.

Minerva: A Vancouver-based Company

Founded by a diverse team of geoscientists, AI researchers, software developers, and geospatial analysts, Minerva aims to bridge the gap between cognitive AI research and practical geospatial applications. Although Minerva is relatively new, its founders laid the groundwork for the company back in the early 2000s. However, due to limited computational capacity at the time, the technology remained under development until recent years.

Today, Minerva offers explainable cognitive AI solutions and geospatial analytics to solve complex problems in mining and natural hazards. Its technology has gained recognition and has been successfully applied in various domains. With this strong foundation, Minerva is expanding its verticals to address insurtech applications, specifically in catastrophe risk modeling and management.

Minerva's Approach to Artificial Intelligence

Minerva's cognitive AI approach differs from the conventional methods commonly associated with AI. Instead of relying solely on neural networks and vast amounts of training data, Minerva aims to train computers to think and reason like human experts. In domains such as geoscience, where data availability is often limited, Minerva offers a revolutionary alternative: knowledge graphs.

Training the Computer to Think Like Human Experts

Traditional AI models heavily rely on large datasets, which are not always readily available in geoscience applications such as landslide and flood modeling. Minerva tackles this challenge by providing tools and support for clients to develop expert-based conceptual models using knowledge graphs. Rather than using numbers, Minerva allows users to describe a particular domain in words, enabling the computer to interpret and reason with human-like understanding.

Knowledge Graphs: An Alternative to Neural Networks

Knowledge graphs serve as a means to organize and structure domain-specific knowledge. They provide a context to the data, allowing for more informed decision-making. Minerva's reasoning engine, known as the reasoner, leverages the power of the knowledge graph to make comparisons of similarity between various physical entities and compile different datasets. The resulting solutions from Minerva's workflow are not only accurate but also completely explainable, offering transparency and an audit trail for each decision made.

Applications of Minerva's Cognitive AI in Insurtech

Minerva's cognitive AI technology has garnered significant attention in the field of insurtech. By applying its reasoning engine and knowledge graphs, Minerva offers innovative solutions for insurers, reinsurers, and real estate investors, empowering them to make informed decisions about natural hazards in their respective domains.

Catastrophe Risk Modeling and Management

The complexity of catastrophe risk modeling requires expert judgment. With limited data availability, Minerva's cognitive AI workflow provides the tools needed to model and manage these risks effectively. By combining various datasets, expert knowledge, and the reasoner's analysis, Minerva enables insurers and reinsurers to assess risks accurately and efficiently.

Flood Risk Assessment in Property and Casualty Insurance

One of the significant applications of Minerva's technology in the property and casualty insurance industry is flood risk assessment. By comparing different properties' flood risk using explainable similarity scores, underwriters can evaluate flood risk more quickly and consistently. This approach significantly improves the accuracy of risk assessment and helps underwriters better understand the potential exposure to natural hazards.

Connecting Investors with the Right Properties

Investing in properties requires a thorough understanding of the associated risks, including natural hazards like flooding. Minerva's cognitive AI technology provides valuable insights by connecting investors with the right properties based on flood risk analysis. By considering a combination of factors such as location, building attributes, adjacent water bodies, and flood forecasts, Minerva enables investors to make informed decisions and manage their exposure to natural hazards effectively.

Wildfire Risk Analysis for Linear Infrastructure Operators

Operators of linear infrastructure, such as power lines and pipelines, face the challenge of assessing wildfire risks to specific segments of their networks. Minerva's technology helps address this challenge by providing explainable risk assessments that integrate various datasets, including historical wildfire data and terrain models. By identifying segments at high risk of wildfires, operators can take proactive measures to mitigate potential damages.

Benefits of Knowledge Graphs in Natural Hazard Analysis

Knowledge graphs offer several advantages when it comes to analyzing natural hazards. By encoding expert knowledge and providing a flexible data storage format, knowledge graphs enhance the effectiveness and efficiency of analysis. Some of the key benefits of using knowledge graphs in natural hazard analysis include:

Encoding Expert Knowledge in the Computer

Knowledge graphs enable the computer to understand and reason with domain-specific knowledge, leveraging expert expertise without the need for extensive training data. Minerva's cognitive AI workflow captures the wisdom of human experts, empowering decision-makers to utilize their insights effectively.

Flexibility in Incorporating Quantitative and Qualitative Data

Knowledge graphs allow the incorporation of both quantitative and qualitative data, providing a comprehensive view of the problem at HAND. This flexibility enables users to tailor their analyses to specific client requirements, enhancing the overall quality and relevance of the results.

Rich Data Storage Format

By structuring data in knowledge graphs, Minerva creates a rich data storage format. This format enables the consolidation of various datasets, from cat model outputs to geospatial information, providing a holistic view of the risk landscape. The context provided by knowledge graphs enhances the value and interpretation of the data.

Complementing Existing Cat Model Outputs

Minerva's cognitive AI workflow complements existing catastrophe (cat) model outputs by providing a contextualized interpretation of the results. By analyzing cat model scores alongside other geospatial data, such as building attributes and watershed characteristics, Minerva enhances the depth and accuracy of risk assessments.

Transparency and Explainability of Results

Minerva's cognitive AI solutions are designed to be completely transparent and explainable. Unlike black-box AI systems that provide cryptic results, Minerva's workflow delivers clear and understandable scores. This transparency allows users to audit the results, understand the underlying reasoning, and make well-informed decisions based on reliable information.

The Five-Step Process of Minerva's Cognitive AI Workflow

Minerva's cognitive AI workflow follows a five-step process to deliver accurate and actionable results. This process ensures that the knowledge graph is built, relevant data sets are integrated, expert knowledge is captured, and the reasoner can analyze the data effectively. The steps involved in Minerva's cognitive AI workflow are as follows:

Step 1: Building the Knowledge Graph

The first step in the workflow is to build a knowledge graph tailored to the specific domain. This process involves structuring and organizing the information obtained from experts. The knowledge graph serves as the foundation for subsequent analysis and reasoning.

Step 2: Integrating Relevant Data Sets

Once the knowledge graph is established, the next step is to integrate relevant data sets into the workflow. Minerva collaborates with cat model vendors and open data sources to obtain the necessary data. Aligning the data with the structure of the knowledge graph ensures that all information is placed into context for analysis.

Step 3: Capturing Expert Knowledge

A crucial element of Minerva's cognitive AI workflow is capturing expert knowledge. Working with domain experts, Minerva's software tools allow for the definition of essential attributes and parameters in the specific domain. The combination of expert knowledge with the contextualized data enhances the accuracy of the subsequent analysis.

Step 4: Applying Reasoning with the Reasoner

The reasoner, Minerva's powerful reasoning engine, processes the integrated data and expert knowledge to generate similarity scores and make informed comparisons. By leveraging the knowledge graph, the reasoner can combine different data sets, evaluate physical entities, and provide valuable insights specific to the problem domain.

Step 5: Delivering Results through Web Dashboards

The final step in Minerva's cognitive AI workflow is the delivery of results to the end-user through web dashboards. These dashboards provide an interactive interface where users can explore the results, drill down into specific details, and gain a comprehensive understanding of the risk analysis. With the ability to access and interpret the results effectively, decision-makers can confidently make informed choices.

Success Factors for Data Analysts and Scientists in the Industry

Data analysis and science are rapidly growing fields, and success in these domains requires a combination of technical skills, domain expertise, and critical thinking abilities. In the context of Minerva and the natural hazards industry, the most useful skill sets for data analysts and scientists include:

• Knowledge of Semantics and Working with Graph Data: A strong understanding of semantics and the ability to work with graph data are essential, as knowledge graphs play a crucial role in Minerva's cognitive AI workflow.
• Domain Expertise: Having expertise in domains such as geoscience, risk assessment, and catastrophe modeling provides a solid foundation for data analysts and scientists in the natural hazards industry.
• Geospatial Data Analysis: Proficiency in analyzing and interpreting geospatial data is essential, as it forms a significant part of the data used in natural hazard analysis.
• Data Visualization: The ability to effectively communicate Data Insights through visualizations is critical. Data analysts and scientists should be skilled in creating intuitive and informative visual representations of complex information.
• Critical Thinking and Problem-Solving: Strong critical thinking skills and the ability to solve complex problems are crucial for data analysts and scientists in the natural hazards industry. This includes the ability to analyze diverse datasets, identify Patterns, and draw Meaningful conclusions.

Conclusion

Minerva's approach to artificial intelligence, leveraging cognitive reasoning and knowledge graphs, offers a Novel solution for the challenges faced in the natural hazards and insurtech domains. By training computers to think like human experts and providing context through knowledge graphs, Minerva's cognitive AI workflow empowers decision-makers to make informed choices regarding catastrophic risk modeling, flood risk assessment, property investment, and linear infrastructure management. The benefits of using knowledge graphs, such as encoding expert knowledge and transparency of results, further enhance the value of Minerva's technology. With the right skill sets and a commitment to innovation, data analysts and scientists can excel in the natural hazards industry, contributing to the effective management of risks and the protection of lives and properties.

[Resource URLs]

1. Minerva: https://www.minervainsights.com/
2. Zesty Flood Score: https://www.zestyflood.com/